{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Code for **\"Blind restoration of a JPEG-compressed image\"** and **\"Blind image denoising\"** figures. Select `fname` below to switch between the two.\n",
    "\n",
    "- To see overfitting set `num_iter` to a large value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import os\n",
    "# os.environ['CUDA_VISIBLE_DEVICES'] = '1'\n",
    "\n",
    "import numpy as np\n",
    "from models import *\n",
    "\n",
    "import torch\n",
    "import torch.optim\n",
    "\n",
    "from torch.autograd import Variable\n",
    "from utils.denoising_utils import *\n",
    "\n",
    "torch.backends.cudnn.enabled = True\n",
    "torch.backends.cudnn.benchmark =True\n",
    "dtype = torch.FloatTensor\n",
    "\n",
    "imsize =-1\n",
    "PLOT = True\n",
    "sigma = 25\n",
    "sigma_ = sigma/255."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## deJPEG \n",
    "fname = 'data/denoising/snail.jpg'\n",
    "\n",
    "## denoising\n",
    "# fname = 'data/denoising/F16_GT.png'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116ac8940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if fname == 'data/denoising/snail.jpg':\n",
    "    img_noisy_pil = crop_image(get_image(fname, imsize)[0], d=32)\n",
    "    img_noisy_np = pil_to_np(img_noisy_pil)\n",
    "    \n",
    "    # As we don't have ground truth\n",
    "    img_pil = img_noisy_pil\n",
    "    img_np = img_noisy_np\n",
    "    \n",
    "    if PLOT:\n",
    "        plot_image_grid([img_np], 4, 5);\n",
    "        \n",
    "elif fname == 'data/denoising/F16_GT.png':\n",
    "    # Add synthetic noise\n",
    "    img_pil = crop_image(get_image(fname, imsize)[0], d=32)\n",
    "    img_np = pil_to_np(img_pil)\n",
    "    \n",
    "    img_noisy_pil, img_noisy_np = get_noisy_image(img_np, sigma_)\n",
    "    \n",
    "    if PLOT:\n",
    "        plot_image_grid([img_np, img_noisy_np], 4, 6);\n",
    "else:\n",
    "    assert False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "Torch not compiled with CUDA enabled",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-8330ff938e7a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m                 need_sigmoid=True, need_bias=True, pad=pad, act_fun='LeakyReLU')\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m     \u001b[0mnet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mfname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'data/denoising/F16_GT.png'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/dip/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36mtype\u001b[0;34m(self, dst_type)\u001b[0m\n\u001b[1;32m    233\u001b[0m             \u001b[0mModule\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m         \"\"\"\n\u001b[0;32m--> 235\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/dip/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/dip/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/dip/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    145\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchildren\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 146\u001b[0;31m             \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    147\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mparam\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parameters\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/dip/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_apply\u001b[0;34m(self, fn)\u001b[0m\n\u001b[1;32m    150\u001b[0m                 \u001b[0;31m# Variables stored in modules are graph leaves, and we don't\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m                 \u001b[0;31m# want to create copy nodes, so we have to unpack the data.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 152\u001b[0;31m                 \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    153\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grad\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m                     \u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_grad\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/dip/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(t)\u001b[0m\n\u001b[1;32m    233\u001b[0m             \u001b[0mModule\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    234\u001b[0m         \"\"\"\n\u001b[0;32m--> 235\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdst_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    237\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/dip/lib/python3.6/site-packages/torch/_utils.py\u001b[0m in \u001b[0;36m_type\u001b[0;34m(self, new_type, async)\u001b[0m\n\u001b[1;32m     36\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mnew_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_sparse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot cast dense tensor to sparse tensor\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnew_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0masync\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/dip/lib/python3.6/site-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_lazy_new\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m    356\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    357\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_lazy_new\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 358\u001b[0;31m     \u001b[0m_lazy_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    359\u001b[0m     \u001b[0;31m# We need this method only for lazy init, so we can remove it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    360\u001b[0m     \u001b[0;32mdel\u001b[0m \u001b[0m_CudaBase\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__new__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/dip/lib/python3.6/site-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_lazy_init\u001b[0;34m()\u001b[0m\n\u001b[1;32m    118\u001b[0m         raise RuntimeError(\n\u001b[1;32m    119\u001b[0m             \"Cannot re-initialize CUDA in forked subprocess. \" + msg)\n\u001b[0;32m--> 120\u001b[0;31m     \u001b[0m_check_driver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_sparse_init\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/dip/lib/python3.6/site-packages/torch/cuda/__init__.py\u001b[0m in \u001b[0;36m_check_driver\u001b[0;34m()\u001b[0m\n\u001b[1;32m     53\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_check_driver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'_cuda_isDriverSufficient'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Torch not compiled with CUDA enabled\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_isDriverSufficient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cuda_getDriverVersion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAssertionError\u001b[0m: Torch not compiled with CUDA enabled"
     ]
    }
   ],
   "source": [
    "INPUT = 'noise' # 'meshgrid'\n",
    "pad = 'reflection'\n",
    "OPT_OVER = 'net' # 'net,input'\n",
    "\n",
    "reg_noise_std = 1./30. # set to 1./20. for sigma=50\n",
    "LR = 0.01\n",
    "\n",
    "OPTIMIZER='adam' # 'LBFGS'\n",
    "show_every = 500\n",
    "\n",
    "if fname == 'data/denoising/snail.jpg':\n",
    "    num_iter=2400\n",
    "    input_depth = 3\n",
    "    figsize = 5 \n",
    "    \n",
    "    net = skip(\n",
    "                input_depth, 3, \n",
    "                num_channels_down = [8, 16, 32, 64, 128], \n",
    "                num_channels_up   = [8, 16, 32, 64, 128],\n",
    "                num_channels_skip = [0, 0, 0, 4, 4], \n",
    "                upsample_mode='bilinear',\n",
    "                need_sigmoid=True, need_bias=True, pad=pad, act_fun='LeakyReLU')\n",
    "\n",
    "    net = net.type(dtype)\n",
    "\n",
    "elif fname == 'data/denoising/F16_GT.png':\n",
    "    num_iter = 3000\n",
    "    input_depth = 32 \n",
    "    figsize = 4 \n",
    "    \n",
    "    net = get_net(input_depth, 'skip', pad,\n",
    "                  skip_n33d=128, \n",
    "                  skip_n33u=128, \n",
    "                  skip_n11=4, \n",
    "                  num_scales=5,\n",
    "                  upsample_mode='bilinear').type(dtype)\n",
    "\n",
    "else:\n",
    "    assert False\n",
    "    \n",
    "net_input = get_noise(input_depth, INPUT, (img_pil.size[1], img_pil.size[0])).type(dtype).detach()\n",
    "\n",
    "# Compute number of parameters\n",
    "s  = sum([np.prod(list(p.size())) for p in net.parameters()]); \n",
    "print ('Number of params: %d' % s)\n",
    "\n",
    "# Loss\n",
    "mse = torch.nn.MSELoss().type(dtype)\n",
    "\n",
    "img_noisy_var = np_to_var(img_noisy_np).type(dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "net_input_saved = net_input.data.clone()\n",
    "noise = net_input.data.clone()\n",
    "\n",
    "\n",
    "i = 0\n",
    "def closure():\n",
    "    \n",
    "    global i\n",
    "    \n",
    "    if reg_noise_std > 0:\n",
    "        net_input.data = net_input_saved + (noise.normal_() * reg_noise_std)\n",
    "    \n",
    "    out = net(net_input)\n",
    "   \n",
    "    total_loss = mse(out, img_noisy_var)\n",
    "    total_loss.backward()\n",
    "        \n",
    "    print ('Iteration %05d    Loss %f' % (i, total_loss.data[0]), '\\r', end='')\n",
    "    if  PLOT and i % show_every == 0:\n",
    "        out_np = var_to_np(out)\n",
    "        plot_image_grid([np.clip(out_np, 0, 1)], factor=figsize, nrow=1)\n",
    "        \n",
    "    i += 1\n",
    "\n",
    "    return total_loss\n",
    "\n",
    "p = get_params(OPT_OVER, net, net_input)\n",
    "optimize(OPTIMIZER, p, closure, LR, num_iter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_np = var_to_np(net(net_input))\n",
    "q = plot_image_grid([np.clip(out_np, 0, 1), img_np], factor=13);"
   ]
  }
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